Application of surrogate modeling methods in simulation-based reliability and performance assessment of civil structures

Structures and infrastructure systems are subjected to various deterioration processes due to environmental or mechanical stressors. Proper performance assessment approaches capable of detecting potential structural damage and quantifying the probability associated with structural failure are required to formulate optimal maintenance and retrofit plans that minimize the risk of failure and maximize the safety of structures. However, due to the presence of several sources of uncertainty that can affect the performance assessment and decision-making processes (e.g., uncertainties associated with loading conditions and performance prediction models), applying probabilistic methods, such as Monte Carlo simulation, is essential. In this context, a large number of simulations is generally required to quantify the low failure probability associated with civil structures. Executing the required number of simulations may be computationally expensive, especially if complex and/or nonlinear structural models (e.g., finite element models) are involved. The use of surrogate modeling tools such as artificial neural networks, polynomial chaos expansion, and kriging can help in reducing the computational costs associated with simulation-based probabilistic analysis. The research proposed herein aims to develop probabilistic approaches for performance assessment and damage detection of structures using advanced simulation-based techniques coupled with surrogate modeling. The proposed methodology is applied to quantify the risk of bridge failure due to flood events considering the impact of climate change. The approach was extended to establish the time-variant flood fragility surfaces for bridges under flood conditions. This approach (a) integrates deep learning neural networks into a simulation-based probabilistic approach to predict the future river streamflow necessary for assessing the flood hazard at the bridge location and (b) simulates the structural behavior of the bridge foundation under sour conditions. In addition, the proposed methodology is used to quantify the reliability of bolted and welded steel connections by integrating finite element analysis and surrogate models. Low-rank tensor approximation and polynomial chaos kriging surrogate models are adopted to perform Monte Carlo simulation and quantify the reliability of the investigated combination connection. Finally, artificial neural networks were used to develop a statistical damage detection and localization approach capable of evaluating the performance of prestressed concrete bridge girders using fiber optic sensors

Modelling of tool wear and metal flow behaviour in friction stir welding (FSW)

Friction Stir Welding (FSW) is a solid-state joining process that was invented in 1991; it is particularly useful for joints difficult to make using fusion techniques. Significant advances in FSW have been achieved in terms of process modelling since its inception. However, until now experimental work has remained the primary method of investigating tool wear in FSW. In this project, two main objectives were set; the first one was to produce a numerical approach that can be used as a useful tool to understand the effect that worn tool geometry has on the material flow and resultant weld quality. The second objective was to provide a modelling methodology for calculating tool wear in FSW based on a CFD model. Initially, in this study, a validated model of the FSW process was generated using the CFD software FLUENT, with this model then being used to assess in detail the differences in flow behaviour, mechanically affected zone (MAZ) size and strain rate distribution around the tool for both unworn and worn tool geometries. Later, a novel methodology for calculating tool wear in FSW is developed. Here a CFD model is used to predict the deformation of the highly viscous flow around the tool, with additional analysis linking this deformation to tool wear. A validation process was carried out in this study in order to obtain robust results when using this methodology. Once satisfied with the tool wear methodology results, a parametric study considering different tool designs, rotation speeds and traverse speeds was undertaken to predict the wear depth. In this study, three workpiece materials were used which were aluminium 6061, 7020 and AISI 304 stainless steel, while the materials used for the tools used were of H13 steel and tungsten-rhenium carbide (WRe-HfC) with different tool designs. The study shows that there are significant differences in the flow behaviour around and under the tool when the tool is worn and it shows that the proposed approach is able to predict tool wear associated with high viscous flow around the FSW tool. With a simple dome shaped tool, the results shows that the tool was worn radially and vertically and insignificant wear was predicted during welding near the pin tip. However, in other regions the wear increased as the weld distance increased. Additionally, from the parametric study that was undertaken for the two tool designs - a dome and a conical shape- the study has found that for both tool designs, wear depth increases with increasing tool rotation speed and traverse speed. It was also shown that, generally, the wear depth was higher for the conical tool design than the dome tool in the pin tip zone. The research concludes that a proposed methodology is able to calculate tool wear associated with high viscous flow around the FSW tool, which could be used as a method for calculating tool wear without the need for experimental trials. The CFD model has provided a good tool for prediction and assessment of the flow differences between un-worn and worn tools, which may be used to give an indication of the weld quality and of tool lifetime. Furthermore, from the results, it can be concluded that this approach is capable of predicting tool wear for different process parameters and tool designs and it is possible to obtain a low wear case by controlling the process parameters

Modelling of tool wear and metal flow behaviour in friction stir welding (FSW)

Friction Stir Welding (FSW) is a solid-state joining process that was invented in 1991; it is particularly useful for joints difficult to make using fusion techniques. Significant advances in FSW have been achieved in terms of process modelling since its inception. However, until now experimental work has remained the primary method of investigating tool wear in FSW. In this project, two main objectives were set; the first one was to produce a numerical approach that can be used as a useful tool to understand the effect that worn tool geometry has on the material flow and resultant weld quality. The second objective was to provide a modelling methodology for calculating tool wear in FSW based on a CFD model. Initially, in this study, a validated model of the FSW process was generated using the CFD software FLUENT, with this model then being used to assess in detail the differences in flow behaviour, mechanically affected zone (MAZ) size and strain rate distribution around the tool for both unworn and worn tool geometries. Later, a novel methodology for calculating tool wear in FSW is developed. Here a CFD model is used to predict the deformation of the highly viscous flow around the tool, with additional analysis linking this deformation to tool wear. A validation process was carried out in this study in order to obtain robust results when using this methodology. Once satisfied with the tool wear methodology results, a parametric study considering different tool designs, rotation speeds and traverse speeds was undertaken to predict the wear depth. In this study, three workpiece materials were used which were aluminium 6061, 7020 and AISI 304 stainless steel, while the materials used for the tools used were of H13 steel and tungsten-rhenium carbide (WRe-HfC) with different tool designs. The study shows that there are significant differences in the flow behaviour around and under the tool when the tool is worn and it shows that the proposed approach is able to predict tool wear associated with high viscous flow around the FSW tool. With a simple dome shaped tool, the results shows that the tool was worn radially and vertically and insignificant wear was predicted during welding near the pin tip. However, in other regions the wear increased as the weld distance increased. Additionally, from the parametric study that was undertaken for the two tool designs - a dome and a conical shape- the study has found that for both tool designs, wear depth increases with increasing tool rotation speed and traverse speed. It was also shown that, generally, the wear depth was higher for the conical tool design than the dome tool in the pin tip zone. The research concludes that a proposed methodology is able to calculate tool wear associated with high viscous flow around the FSW tool, which could be used as a method for calculating tool wear without the need for experimental trials. The CFD model has provided a good tool for prediction and assessment of the flow differences between un-worn and worn tools, which may be used to give an indication of the weld quality and of tool lifetime. Furthermore, from the results, it can be concluded that this approach is capable of predicting tool wear for different process parameters and tool designs and it is possible to obtain a low wear case by controlling the process parameters

Laser-induced forward transfer (LIFT) of water soluble polyvinyl alcohol (PVA) polymers for use as support material for 3D-printed structures

The additive microfabrication method of laser-induced forward transfer (LIFT) permits the creation of functional microstructures with feature sizes down to below a micrometre [1]. Compared to other additive manufacturing techniques, LIFT can be used to deposit a broad range of materials in a contactless fashion. LIFT features the possibility of building out of plane features, but is currently limited to 2D or 2½D structures [2–4]. That is because printing of 3D structures requires sophisticated printing strategies, such as mechanical support structures and post-processing, as the material to be printed is in the liquid phase. Therefore, we propose the use of water-soluble materials as a support (and sacrificial) material, which can be easily removed after printing, by submerging the printed structure in water, without exposing the sample to more aggressive solvents or sintering treatments. Here, we present studies on LIFT printing of polyvinyl alcohol (PVA) polymer thin films via a picosecond pulsed laser source. Glass carriers are coated with a solution of PVA (donor) and brought into proximity to a receiver substrate (glass, silicon) once dried. Focussing of a laser pulse with a beam radius of 2 µm at the interface of carrier and donor leads to the ejection of a small volume of PVA that is being deposited on a receiver substrate. The effect of laser pulse fluence , donor film thickness and receiver material on the morphology (shape and size) of the deposits are studied. Adhesion of the deposits on the receiver is verified via deposition on various receiver materials and via a tape test. The solubility of PVA after laser irradiation is confirmed via dissolution in de-ionised water. In our study, the feasibility of the concept of printing PVA with the help of LIFT is demonstrated. The transfer process maintains the ability of water solubility of the deposits allowing the use as support material in LIFT printing of complex 3D structures. Future studies will investigate the compatibility (i.e. adhesion) of PVA with relevant donor materials, such as metals and functional polymers. References: [1] A. Piqué and P. Serra (2018) Laser Printing of Functional Materials. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA. [2] R. C. Y. Auyeung, H. Kim, A. J. Birnbaum, M. Zalalutdinov, S. A. Mathews, and A. Piqué (2009) Laser decal transfer of freestanding microcantilevers and microbridges, Appl. Phys. A, vol. 97, no. 3, pp. 513–519. [3] C. W. Visser, R. Pohl, C. Sun, G.-W. Römer, B. Huis in ‘t Veld, and D. Lohse (2015) Toward 3D Printing of Pure Metals by Laser-Induced Forward Transfer, Adv. Mater., vol. 27, no. 27, pp. 4087–4092. [4] J. Luo et al. (2017) Printing Functional 3D Microdevices by Laser-Induced Forward Transfer, Small, vol. 13, no. 9, p. 1602553